Stacked Ranking Feature Cluster Machine Learning (Srfcml): A Novel Method of Career Planning of College Students Based on Career Interest Assessment and Machine Learning

Abstract

Career interest assessment, powered by machine learning algorithms, revolutionizes the way individuals explore and align with career paths. By analyzing vast datasets encompassing factors such as skills, preferences, personality traits, and job market trends, machine learning models can provide personalized career recommendations tailored to individual strengths and aspirations. These algorithms leverage advanced techniques such as natural language processing (NLP) to interpret self-assessment responses and match them with suitable career options. Additionally, machine learning algorithms continuously refine their recommendations based on user feedback and real-world outcomes, ensuring accuracy and relevance over time. This paper presents a novel approach to career planning for college students, integrating career interest assessment with machine learning techniques, specifically utilizing the Stacked Ranking Feature Cluster Machine Learning (SRFcML) model. The proposed framework leverages large-scale datasets encompassing diverse factors such as academic performance, skills, interests, and industry trends to provide personalized career recommendations. Through the application of machine learning algorithms, including clustering and ranking techniques, the SRFcML model identifies relevant clusters of career options and ranks them based on individual preferences and aptitudes. This approach enables college students to explore and prioritize career paths aligned with their unique strengths and aspirations. Simualtion results demonstrated that the effectiveness of the proposed framework was evaluated, yielding promising numerical results. For instance, based on self-assessment responses and academic performance data, the SRFcML model achieved an average accuracy of 85% in recommending suitable career paths for college students. Furthermore, in a comparison with traditional career planning methods, the proposed framework demonstrated a 30% improvement in the alignment between recommended career options and students' preferences. Additionally, user satisfaction surveys revealed a high level of confidence and trust in the recommendations provided by the SRFcML model, with 90% of participants expressing satisfaction with the accuracy and relevance of the career suggestions.